The subject matter of this application is related to the subject matter of U.S. patent application Ser. No. 08/676,660, entitled xe2x80x9cMethod and Apparatus for Fast Detection of Spiculated Lesions in Digital Mammograms,xe2x80x9d filed on Jul. 10, 1996 and assigned to the assignee of the present invention. The above application is hereby incorporated by reference into the present application.
The present invention relates to the field of computer aided diagnosis of abnormal lesions in medical images. In particular, the invention relates to a fast algorithm for detecting masses in a digital mammogram to assist in the detection of malignant breast cancer tumors at an early stage in their development.
Breast cancer in women is a serious health problem, the American Cancer Society currently estimating that over 180,000 U.S. women are diagnosed with breast cancer each year. Breast cancer is the second major cause of cancer death among women, the American Cancer Society also estimating that breast cancer causes the death of over 44,000 U.S. women each year. While at present there is no means for preventing breast cancer, early detection of the disease prolongs life expectancy and decreases the likelihood of the need for a total mastectomy. Mammography using x-rays is currently the most common method of detecting and analyzing breast lesions.
The detection of suspicious portions of mammograms is an important first step in the early diagnosis and treatment of breast cancer. FIG. 1A shows a continuum of potentially cancerous shapes found in mammograms, ranging from sharply defined masses on the left, moving rightward to somewhat spiculated (i.e., stellar-shaped) masses, mostly spiculated masses, highly spiculated masses, and then finally to pure spiculations on the right.
Sharply defined masses such as those at the left of FIG. 1A are rarely associated with malignant tumors, while the presence of spiculated masses is a strong indicator of malignancy. Pure spiculations, however, are often found among normal fibrous breast tissue and may not indicate a cancerous condition at all. Overall, both the mass qualities and xe2x80x9cspiculatednessxe2x80x9d qualities of shapes found in mammograms must be analyzed in locating suspicious portions of the mammogram.
While it is important to detect the suspicious portions of an x-ray mammogram as early as possible, i.e. when they are as small as possible, practical considerations can make this difficult. In particular, a typical mammogram may contain myriads of lines corresponding to fibrous breast tissue, and the trained, focused eye of a radiologist is needed to detect suspicious features among these lines. Moreover, a typical radiologist may be required to examine hundreds of mammograms per day, leading to the possibility of a missed diagnosis due to human error.
Accordingly, the need has arisen for a computer-assisted diagnosis (CAD) system for assisting in the detection of abnormal lesions in medical images. The desired CAD system digitizes x-ray mammograms to produce a digital mammogram, and performs numerical image processing algorithms on the digital mammogram. The output of the CAD system is a highlighted display which directs the attention of the radiologist to suspicious portions of the x-ray mammogram. The desired characteristics of a CAD system are high speed (requiring less processing time), high sensitivity (the ability to detect subtle suspicious portions), and high specificity (the ability to avoid false positives).
Many algorithms for processing digital mammograms in CAD systems start by processing the digital mammogram to locate masses (or xe2x80x9cdensitiesxe2x80x9d). After this step, the xe2x80x9cspiculatednessxe2x80x9d of these masses is characterized. See Yin et. al., xe2x80x9cComputerized Detection of Masses in Digital Mammograms: Analysis of Bilateral Subtraction Images,xe2x80x9d Med. Phys. 18(5), September/October 1991, pp. 955-963, and Sahiner et. al., xe2x80x9cClassification of Masses on Mammograms Using a Rubber-Band Straightening Transform and Feature Analysis,xe2x80x9d Medical Imaging 1996, SPIE Symposium on Medical Imaging (San Diego, Calif.), Paper No. 2710-06 at p. 204, the contents of which are hereby incorporated by reference into the present application.
A key shortcoming of the above serial approach, in which masses are first detected and then analyzed in a subsequent step, is that some very suspicious shapes are not recognized. In particular, those masses which are small, but which are highly spiculated, often do not survive the xe2x80x9cfirst cutxe2x80x9d of the mass detection routine, which will not recognize masses having density characteristics below a certain threshold. This shortcoming was recognized by Nico Karssemeijer in xe2x80x9cRecognition of Stellate Lesions in Digital Mammograms,xe2x80x9d Digital Mammography: Proceedings of the 2nd International Workshop on Digital Mammography, York, England, Jul. 10-12, 1994 (Elsevier Science 1994), pp. 211-219, the contents of which are hereby incorporated by reference into the present application. There, Karssemeijer proposes an algorithm for the direct detection of spiculations (xe2x80x9cstellate patternsxe2x80x9d) in a digital mammogram without assuming the presence of a central mass.
Another method for the direct detection of spiculations in digital mammograms is provided in Kegelmeyer et. al., xe2x80x9cComputer-aided Mammographic Screening for Spiculated Lesions,xe2x80x9d Radiology 191:331-337 (1994), the contents of which are hereby incorporated by reference into the present application. Yet another method for the direct detection of spiculations, along with linear classification steps which use both mass and spiculation information in identifying suspicious portions of the digital mammogram, is provided by Roehrig et. al. in the above referenced U.S. Patent Application entitled xe2x80x9cMethod and Apparatus for Fast Detection of Spiculated Lesions in Digital Mammograms.xe2x80x9d
One improvement which may be incorporated into CAD systems is further integration and symmetry between of the steps of mass detection and spiculation detection. Such integration and symmetry would provide for more efficient programming of the CAD system, more efficient processing by the CAD system, and reduced memory requirements. In particular, it would be desirable to execute both mass detection and spiculation detection steps using the same or similar computation engines in the CAD system. Additionally, it would be desirable to harness algorithmic advances made in spiculation detection algorithms by applying them to mass detection algorithms.
Accordingly, it is an object of the present invention to provide a fast computer-assisted diagnosis (CAD) system for assisting in the identification of suspicious masses and spiculations in digital mammograms, the CAD system being capable of producing an output which directs attention to suspicious portions of the x-ray mammogram for increasing the speed and accuracy of x-ray mammogram analysis.
It is a further object of the present invention to provide a method for adapting a spiculation detection algorithm for use in a mass detection algorithm, for increased symmetry and integration of CAD system algorithms, and for adapting algorithmic advances in spiculation detection algorithms to mass detection algorithms.
These and other objects of the present invention are provided for by an improved CAD system capable of detecting masses in a digital mammogram image, wherein a gradient image is created from the digital mammogram, and wherein information in the gradient image is then processed for identifying masses. In a preferred embodiment, a portion of a spiculation detection algorithm is applied to the gradient image for identifying masses.
A spiculation detection algorithm normally comprises a line detection portion and a post-line detection portion. However, in a preferred embodiment, the post-line detection portion of the spiculation detection algorithm is applied to a gradient image for identifying masses, instead of being applied to a line image for identifying spiculations. Thus, instead of being provided with line and direction parameters, the post-line detection portion of the spiculation detection algorithm is provided with gradient magnitude and gradient direction parameters. The post-line detection portion of the spiculation detection algorithm then operates normally, except that its output corresponds to mass location and mass density information instead of spiculation location and spiculation intensity information.
Advantageously, computer programs which have already been written for spiculation detection may, with minor modifications, be ported into mass detection programs. Furthermore, advances in the speed and accuracy of spiculation detection algorithms may be applied for use in creating faster and more accurate mass detection algorithms.
When a post-line detection portion of a spiculation detection algorithm has been adapted according to a preferred embodiment, the resulting method of detecting masses operates as follows. A gradient plane is computed from the digital mammogram, each pixel of the gradient plane having gradient magnitude and gradient direction information. A set of edge pixels S in the gradient plane is selected by selecting those pixels having a gradient magnitude greater than a first threshold. A set of candidate pixels in the digital mammogram image is then selected, and, for each candidate pixel xe2x80x9cicandxe2x80x9d, a first density metric G1icand is computed. The metric G1icand, termed a density magnitude metric, is computed according to the steps of (a) selecting a neighborhood of pixels NHicand around the candidate pixel, (b) selecting a small region Ricand around the candidate pixel, (c) selecting a first set of pixels in the neighborhood NHicand having gradient directions pointing toward the small region Ricand and being members of the set S having a gradient magnitude greater than a predetermined lower threshold, and (d) counting the number of pixels in the first set, wherein the first density metric G1icand is proportional to the number of pixels in the first set.
A second density metric G2icand, termed a mass isotropy metric, is also computed for each candidate pixel icand, according to the steps of (a) selecting K spatial bins (icand,k) extending radially from the candidate pixel and being arranged in a radially symmetric manner around the candidate pixel, (b) for each pixel (icand, jpoint) of the first set of pixels, identifying the spatial bin (icand, k) in which the pixel (icand,jpoint) is located, (c) computing a number of pixels nicand,k in each spatial bin (icand,k), and (d) analyzing the statistical distribution of the number nicand,k as k is varied, wherein the mass isotropy metric G2icand is proportional to the number of values k for which ni,k is greater than a median value for random gradient orientations. Finally, the density magnitude and mass isotropy metrics G1 and G2 are evaluated according to a linear classifier or neural network method for determining the locations and intensities of suspicious masses in the digital mammogram.